Vehicle routing guidance to an authoritative location for a point of interest

ABSTRACT

An authoritative candidate is selected for determining a location of a point of interest (POI). Source data including name, address, and location for POIs is received from multiple data sources. The received data is normalized for ease of comparison, and coordinates for each candidate are compared to coordinates of other candidates to determine which candidate if any is an authoritative location for the POI. The candidate locations are compared using two models a metric-based scoring system and a machine learning model that may utilize a gradient boosted decision tree. The authoritative candidate can be used to render digital maps that include the POI. In addition, the authoritative candidate&#39;s location can be used to provide vehicle route guidance to the POI.

BACKGROUND Field

The described embodiments relate generally to providing vehicle routingguidance to points of interest, and more particularly to selecting aparticular location for a point of interest (POI) given multiplecandidate locations.

Description of Related Art

Locations of objects (known often as points of interest, or POIs) on adigital map are typically specified using a coordinate system such aslatitude and longitude. A data provider might report, for example, thata particular gas station is located at a specific latitude andlongitude. Alternatively, the data provider might simply provide astreet address for the POI, leaving it to a map generator to convert, or“geocode,” the address into a coordinate system to place on the map.

In many real-world situations, there are multiple distributors of mapdata, and each may provide incomplete or different location informationfor a particular POI. This then requires the map renderer or otherconsumer of this location data to make a choice about which set oflocation data to give credence to. One way this is typically done is byidentifying and removing outliers in the data—that is, discardingcandidates who are more than some threshold distance away from theremaining candidates.

SUMMARY

Described embodiments enable selection of an authoritative candidate fordetermining a location of a point of interest. Source data includingname, address, and location for POIs is received from multiple datasources. The received data is normalized for ease of comparison, and ifmore than two candidate locations for a POI exist, coordinates for eachcandidate are compared to coordinates of other candidates using ametric-based scoring system and a machine learning model, which togetherconstitute an overall POI location selection system.

The metric-based scoring system utilizes a number of metrics or criteriathat are applied to each candidate location to assess the accuracy ofthat data point. Metrics may be binary, categorical, or continuousvalues. Metrics may include, but are not limited to, across-the-roadconsensus (XTR consensus), building footprint consensus (BF consensus),the distance of a candidate location from the nearest road segment(DFR), nearest same segment consensus (NSS), and nearest segmentpopularity (NSP). Consensus metrics are binary or categorical metricsthat determine whether the candidate location is included in a consensusgroup of locations. Candidate locations that qualify as consensuslocations (for the XTR, BF, and NSS consensus metrics) are a member of aplurality or majority of candidate locations that satisfy a particulargeographic constraint. For example, candidate locations that satisfy theXTR consensus metric are those that are part of a majority of candidatelocations on a same side of a road segment, BF consensus locations arethose that are part of a majority or plurality of candidate locationslocated inside of the same building footprint, etc.

Each candidate location is scored based on each of the metrics includedin the scoring system. The particular score assigned to each metricvalue may be adjustable such that the data consumer can determine therelative importance of each metric to the authority or accuracy of acandidate point. Thus, the metric-based scoring system provides aflexible and understandable system for determining authoritativecandidate locations.

The group of candidate locations for a particular POI may also beevaluated by a machine learning model. The machine learning modeloperates by determining feature vectors for each candidate location. Thefeature vector for a particular candidate location includes per-locationfeatures and per-pair features. Features may be binary, categorical, orcontinuous. Per-location features are a category of features that arecalculated based on the data for candidate location alone, and typicallynot in comparison to other candidate locations, though there may beexceptions. For example, per-location features may include, but are notlimited to whether a candidate location is on a road segment (OTR), thedistance of a candidate location from the nearest road segment (DFR),POI category (e.g. café, restaurant, museum, park, etc.), whether acandidate location inside a building footprint (IB), nearest segmentpopularity (NSP) etc.

Per-pair features are evaluated on each combination of candidatelocation pairs in a set of candidate locations. For example, if thereare three POI data providers in a particular data set, each providerproviding a candidate location for each POI, then there would be threepossible pairs of candidate locations for each POI and the featurevector would include two sets of per-pair features for each candidatelocation. Per-pair features may include, but are not limited to, whetherthe pair is across a road segment from each other (XTR), the distancebetween the pair, whether the pair is located in the same building (SB),whether the pair has the nearest same segment (NSS), etc.

To train the machine learning model, feature vectors are calculated forsets of training data, where the real location of the POI is known (e.g.curated by the POI data consumer or another entity). A gradient boosteddecision tree (GBDT) algorithm may then be used to determine a decisiontree for the training data set. The resulting decision tree may be amulticlass classifier or a set of binary classifiers. The GBDT algorithmmay include an objective function for scoring model iterations thatincludes a loss term and regularization term. Other supervised learningalgorithms typically used for classification may also be used, such asrandom-forest algorithms. In embodiments, with a single multiclassclassifier, a single GBDT is learned that classifies a set of candidatelocations to determine which location, if any, of the candidatelocations is authoritative. In embodiments including multiple binaryclassifiers, a GBDT may be learned for each provider in the data set andfor the case where no provider provides an authoritative location. Inthis case, each GBDT solves a binary classification problem for whethereach provider has provided an authoritative point. For example, if thereare three POI data providers a GBDT will be trained for each dataprovider to determine whether a candidate location provided by thatprovider is an authoritative point. In addition, a GBDT will be trainedfor the case where no provider is an authoritative point for a total offour GBDTs.

After the machine learning model has been trained, any new POI data fromthe set of providers is evaluated using the machine learning model. EachGBDT classifies a corresponding candidate location as authoritative ornon-authoritative, where the corresponding candidate location wasprovided by the provider corresponding to that GBDT. The GBDT for noprovider may also be evaluated using a separate set of features.Alternatively, a single multiclass GBDT may be used to classify acandidate location as authoritative from the plurality of providedcandidate locations. Each GBDT outputs a classification and acorresponding confidence value. An authoritative candidate location canthen be chosen based on a set of confidence criteria, or if theconfidence criteria is not satisfied by any of the GBDT classificationsthe machine learning model may determine that there is no authoritativecandidate location from the set of provided locations.

Once the results of the machine learning model and the metric-basedscoring model have been independently determined, they are compared todetermine whether the same candidate location was selected by eachmodel. If the model selections differ a set of selection criteria isused to determine candidate location is the most authoritativecandidate, or whether no authoritative candidate can be determined.

Once the authoritative candidate is chosen it can be used to renderdigital maps that include the POI. In addition, the authoritativecandidate's location can be used to provide vehicle route guidance tothe POI.

Additional features of the various embodiments are described furtherbelow, and nothing in this summary is intended as limiting in scope, oras indicating that a particular feature is essential or otherwiserequired.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of multiple potential candidate locationsfor a point of interest according to one embodiment.

FIG. 2 illustrates a system for determining an authoritative location ofa point of interest in accordance with one embodiment.

FIG. 3 illustrates a method for determining an authoritative location ofa point of interest in accordance with one embodiment.

FIG. 4 illustrates a method for determining an authoritative location ofa point of interest using a metric-based scoring system in accordancewith one embodiment.

FIG. 5A is a conceptual illustration of determining the XTR metric foran example of multiple potential candidate locations in accordance withone embodiment.

FIG. 5B is a conceptual illustration of determining the BF metric for anexample of multiple potential candidate locations in accordance with oneembodiment.

FIG. 5C is a conceptual illustration of determining the nearest roadsegment to calculate the DFR, NSS, and NSP metrics for an example ofmultiple potential candidate locations in accordance with oneembodiment.

FIG. 6 illustrates a method for training a machine learning model fordetermining an authoritative location in accordance with one embodiment.

FIG. 7A-7B are conceptual illustrations of the application of authoritycriteria for an example of multiple potential candidate locations anddiffering example curated locations in accordance with one embodiment.

FIG. 8 illustrates a method for determining an authoritative location ofa point of interest using a machine learning model in accordance withone embodiment.

FIG. 9 is a diagram illustrating an example of a computer system uponwhich described embodiments may be implemented.

FIG. 10 is a diagram illustrating an example of a mobile computingdevice upon which embodiments described may be implemented.

DETAILED DESCRIPTION

One use case we explore here for purposes of illustrating variousembodiments involves a travel coordination system. Other use casesexist—in general, any application that benefits from having knowledge ofa POI's location with respect to nearby road segments—and the particularexamples that flow throughout this description should be understood tobe given for ease of illustration, and not as a limitation of scope.

Considering this example use case then, assume a user of atransportation coordination system's client application, whom we shallrefer to as a rider, wants to secure a ride from some first location,and to be dropped off at the Has Bean Coffee Shop in Las Vegas, Nev.While it may be possible that the rider knows the actual and preciselatitude and longitude of the drop off point, it is more likely—and weassume, for purposes of this discussion—that the rider knows only thename and perhaps the general location of the coffee shop. Once the riderenters this information into the client software, or in a variation ofthe use case, tells the driver the information, the correct location ofthe Has Bean Coffee Shop in Las Vegas has to be determined so that thedriver can be provided with accurate navigation directions to thecorrect drop off point.

A problem arises when there are multiple sources of data—such asmultiple providers of name/location matching information—that giveconflicting information about the precise location of a particular pointof interest. There may be variation in the name of the point ofinterest, for example—“Has Bean Coffee Shop,” “Has Bean Coffee,” and“Has Bean Shop” might be names provided by three different data sourcesto refer to the same actual retail establishment. Similarly, theaddresses provided by vendors may not match, and the latitude/longitudeor other coordinates provided by the vendors may not match the actualcoordinates of the street address, even if the street address iscorrect.

While this problem could potentially be avoided by better data curationon the part of vendors, we assume that the problem exists as it doestoday—that is, the data about POI locations supplied by differentvendors is often inconsistent, and frequently wrong. Given thatinconsistency, a number of different possible locations for the POI mayexist, and one has to be chosen as authoritative before navigationrouting can be performed. The question then is how to choose anauthoritative candidate from among these different candidate locationsfor a particular POI.

As noted above, one solution to this problem has been to identifycandidate points that are outliers—that is, they are too far from theother candidate points to be considered potentially correct. Forexample, FIG. 1 illustrates four street blocks defined by theintersection of two streets. In this case, the intersection includesfour street segments intersecting 102 a, 102 b, 102 c, and 102 d.Although the intersection only involves two streets, street segmentsmore specifically define a section of street between two particularintersections. FIG. 1 also illustrates nine potential candidatelocations for the relevant POI 100 a, 100 b, 100 c, 100 d, 100 e, 100 f,100 g, 100 h, and 100 i. In this example, these may be potentiallocations of the Has Bean Coffee Shop.

As can be seen in FIG. 1, candidate point 100 d is a distance outlier,located relatively far from the remaining candidate points 100 a-100 cand 100 e-100 i. Using methods that simply neglect distance outliers,candidate point 100 d would be dropped from consideration when choosingthe ultimate authoritative location, and one of the remaining eightcandidates would be chosen instead.

Unlike the methods just described, embodiments described herein takeinto account a variety of mapping features and metrics in a two partsystem for POI location selection. For example, candidate point 100 i ismuch closer to the average candidate location and could easily survive adistance outlier test unscathed. But, from a travel coordination systemuser's perspective, being dropped off at point 100 i would representsignificantly more inconvenience than being dropped off at point 100 b,assuming that the actual position of the POI was one of 100 a-100 c—inthe case of a drop off at point 100 i, the rider would be forced tocross the intersection, which could be time-consuming at least, anddangerous at worst. Furthermore, a navigation algorithm used to guidethe driver to the drop off point would consume unnecessary time inrouting the driver and rider to a point on the wrong side of theintersection, which might add cost as well as inconvenience to bothparties.

FIG. 1 for illustration purposes shows nine candidate positions for asingle POI. In an actual POI location selection system, there may alarger or smaller number of candidate locations. In addition to thecandidate positions 100 a-100 i, FIG. 1 also illustrates a number of mapfeatures, from a map data provider including street segments 102 a-102d, building footprints 104 a-140 d and parking lot 106. In addition tothe illustrated features the map data may also include additional typesof features. Furthermore, the map features and candidate positions for aPOI are illustrated in a two dimensional fashion for ease ofillustration only. Map data may be provided in a three dimensionalformat depending on the embodiment.

FIG. 2 illustrates a system 200 in accordance with one embodiment fordetermining an authoritative location of a POI. Multiple sources 202,204, 206 of POI data provide their data to a data normalizer 208 ofsystem 200. Although only three data sources are shown for illustrativeease, any number of source providers can be used. In some embodiments,individual users can be data providers in a crowd-sourcing system.Alternatively, or in addition to crowd-sourced data, curated data can beprovided by multiple vendors, for example as part of a commercialrelationship. Typically, this data includes at least a name, address andcoordinates of the POI, and may additionally include other information,e.g., opening times, pricing, and the like. Data from these providerscan be accessed programmatically, for example via web-based interfaces.

Data normalizer 208 receives the POI data from the data sources 202,204, 206 and normalizes it. For example, data may be reported by thedata sources in different formats having different file types, namingconventions, address specifications, coordinate systems, and the like.Normalization allows data from the various sources to be comparedagainst each other for purposes of identifying which candidate should beconsidered authoritative. In one embodiment, Apache Thrift's map featureinterface can be used to assist with data normalization. Additionally,the data normalizer 208 may apply secondary features for a particularPOI. For example, data providers 202, 204, and 206 may provide labelsfor a particular POI indicating that a POI is a café, restaurant, andhistorical site respectively. The data normalizer 208 might then apply aset of rules to determine a single label for the POI based of theprovided labels. Alternatively, the POI may be categorized by allprovided labels.

Map data source 210 includes base map data and road network data. Basemap data includes feature information that is typically used to render adigital map, and the particular features included in the data source 210can vary in different embodiments depending on the desire of theimplementer and the purpose for which the map is being used. Data source210 includes road network data, which describes networks of roadsegments and navigation rules for the segments, which allows anavigation subsystem 212 to provide routing guidance from a firstlocation to a second location. Additionally, map data source may includeother geographic features or landmarks that would not be provided by POIproviders. These may include but are not limited to, buildingfootprints, parking lot footprints, street width data, building heightdata, and other geographic information.

Navigation subsystem 212 provides routing guidance from a first locationto a second location. In one embodiment, and as contemplated in the usecase example described here, navigation subsystem 212 is part of atravel coordination system that connects riders with drivers so thatdrivers can provide riders with trips from a first location to a secondlocation (in this case, a POI). In other embodiments, navigationsubsystem 212 is not part of a travel coordination system, and insteadprovides navigation data to the POI for any driver on request, or aspart of a service offered by an implementer of system 200 directly todrivers or to intermediaries who in turn provide such a service todrivers. Note also that while we refer to “drivers” throughout thisdescription, the described embodiments have equal application toproviding routing information to pedestrians, cyclists, or any otherpotential user.

The training data store 214 is a data repository storing data for use intraining the machine learning model 218. The training data store 214 maycontain curated data for a number of POIs including at least the name,address, and specific location coordinates of a POI. The curatedcoordinate for a given POI may be an exact location of the POI or abeneficially located drop-off location close to the POI, depending onthe use case of the POI selection system. For example, in pick-up anddrop-off use cases it may be beneficial to use an ideal drop-offlocation as the curated data point. The training data stored in thetraining data store 214 may be curated by the entity implementing thePOI selection or received from a trusted third party source. Thetraining data store 214 may be configured to store tens of thousands ofcurated POI locations or more depending on the embodiment.

POI location determination engine 216 uses the normalized POI datareceived from the various data sources, in combination with the base mapdata and road network information from map data source 210 to select anauthoritative candidate to use when providing navigation routing to adriver and for rendering on a map, for example on a driver's device orrider's device. The POI location determination engine 216 includes amachine learning model 218 and a metric-based scoring system 220 thatare used together to select an authoritative candidate position for aPOI.

In some embodiments, the machine learning model 218 is a gradientboosted decision tree (GBDT) model, and is trained utilizing analgorithm such as XGboost. Other embodiments may utilize otherclassification models such as a random forest classifier and/or mayutilize training algorithms other than XGboost. Typically trainingalgorithms include a loss function and a regularization function. One ofskill in the art will appreciate that many supervised learningalgorithms could be used to classify the training data. The training andoperation of the machine learning model 218 are detailed in FIGS. 6 and8 respectively.

Referring to the example of the travel coordination system, in which arider would like a driver to take her to the Has Bean Coffee Shop in LasVegas, in one embodiment, a rider uses a client application on a mobiledevice, such as mobile device 1000 described below with respect to FIG.10. Using device 1000, the rider inputs a POI identifier, which in thiscase is “Has Bean Coffee Shop.” The request is received by navigationsubsystem 212 for coordinating travel between the rider and a driver.

In one embodiment, the selection by system 200 of an authoritative POImay be done at the time a request is received by system 200 from a riderfor a POI. In other embodiments, identification of an authoritative POIcandidate is done prior to the point at which a request is received froma rider. For example, system 200 may process and determine authoritativecandidates each time data is received from a data source 202, or mayperform the processing in batch, either periodically, or once aparticular number of candidates have been received. Alternatively,system 200 may determine authoritative candidates when additionaltraining data has been curated and stored in the training data store214. The particular timing for determination of the authoritativecandidate is left to the discretion of the implementer, and for purposesof description in FIG. 3, we presume that the determination is madeprior to receiving the request from the rider.

In FIG. 3, the illustrated method begins with the receipt 302 of datafrom the various data sources. As described above, different dataproviders may provide data in different formats with variation in names,address, or coordinates, and system 200 thus normalizes 304 the data tomake comparison feasible.

In some embodiments, if 306, for a particular POI, there is only onecandidate location, then no further analysis need be done, as the solecandidate is the authoritative candidate 309. However, in someembodiments, the machine learning model 216 may still be applied to thesingle candidate to determine the certainty that the particularcandidate is close enough to the actual POI location when compared tothe possibility that the no candidate location is accurate.

Alternatively, if 306 there is more than one candidate for the POI'slocation, POI location determination engine 216 applies 308 the machinelearning model 218 to the normalized POI location candidates.Simultaneously, or in series, the POI location determination engine 216also applies 310 the metric-based scoring system 220. The machinelearning model 218 then outputs 312 a selection of an authoritativecandidate location from the plurality of candidate locations for the POIand an accompanying confidence score for the selection. The metric-basedscoring system 314 also outputs a second selection of an authoritativecandidate from the plurality of candidate locations and, optionally, asecond confidence score associated with the second selection.

The POI location selection engine 216 then compares 316 the twoselections from the machine learning model 218 and the metric-basedscoring system 220 and determines whether both processes have resultedin the same selection of an authoritative candidate. If 318, theselected candidates are the same then that candidate is selected as theoverall authoritative candidate. If 320, selected candidates aredifferent then the POI location selection engine 216 applies a set ofselection criteria to determine which of the two selection should beselected as the final authoritative candidate.

Selection criteria are a set of criteria used by the system 200 todetermine, which of the machine learning selection and the metric-basedselection should be selected as the final authoritative candidate for aPOI. In some embodiments, both selections are associated with aconfidence value and so the selection criteria may simply be choosingthe selection with the highest confidence. Alternatively, if only themachine learning selection has an associated confidence level, theselection criteria may instead be implemented as a confidence thresholdfor the machine learning selection. If the confidence for the machinelearning selection is greater than the threshold, the machine learningselection will be selected as the authoritative candidate. If theconfidence is lower than the threshold, then the metric-based selectionmay be selected as the authoritative candidate. In another embodiment,different confidence thresholds may be applied to each selection and ifneither confidence value meets the threshold the selection process maybe determined to be inconclusive.

In some embodiments, additional variations of either the machinelearning model 218 or the metric-based scoring system 220 may beutilized by the POI location determination engine 216. In these cases,more complex comparisons 316 and selection criteria may be utilized toselect an authoritative candidate from amongst multiple model outputs.

After an authoritative candidate location is selected 320, a series ofoptional steps may be completed if the system 200 is a travelcoordination system. As noted, we have assumed for purposes of thisdiscussion that the authoritative candidate is selected prior toreceiving the rider's request for coordination of transportation to thePOI. Thus, following selection 318 of the authoritative candidate, therider's request is received 322. In one embodiment, the mobile device1000, executing a software application for rider-side coordination oftravel requests, can display a map of the location surrounding the POI,with the authoritative candidate rendered 324 on the map. In oneembodiment, system 200 then uses the navigation subsystem 212 tocoordinate a ride between the rider and a driver, and in one embodimentprovides 326 navigation routing to the authoritative POI location, forexample by providing routing information to a driver's device.

FIG. 4 illustrates a method for determining an authoritative location ofa point of interest using a metric-based scoring system in accordancewith one embodiment. FIG. 4 details steps 310 and 314 of FIG. 3. Firstthe metric-based scoring system 220 receives 402 candidate locations andassociated data for a particular POI. The candidate locations aregeo-coordinates specifying a proposed location for the POI and may alsoinclude address information. The associated data may include but are notlimited to a POI type (e.g. café, restaurant, historical site,recreational area, park, museum, etc.), sublocation information (e.g.metadata indicating that the POI is inside of a mall, park, museum orother larger entity), or any other detailing information that might beprovided by a POI data provider.

The metric-based scoring system 220 also obtains 404 map data from mapdata store 210 and proceeds to evaluate 406 the received candidatelocations against a predetermined set of metrics. Depending on theparticular metric, or the embodiment, a metric may be evaluated on abinary, categorical, or continuous scale such that particular candidatelocation for a POI receives a score for each metric it is evaluatedagainst. Depending on the embodiment, any number of metrics may be usedincluding but not limited to one or more of the following options.

Across-the-road (XTR) Consensus: Across the road consensus may be abinary or a categorical metric, depending on the embodiment, thatidentifies whether a given candidate location is a member of a consensusof locations in relation to which side of a road a location is locatedon. For example, if there are a total of four candidate locations andthree of them are on one side of a road segment then each of those threecandidate locations would satisfy the XTR metric and receive acorresponding score. The aforementioned result would occur because thethree points on the same side of the road comprise the majority ofpoints. Alternatively, only a plurality of points are required tosatisfy the XTR metric. In another embodiment, different scores areassigned based on whether the candidate locations are a member of amajority consensus or a plurality consensus. In yet another embodiment,the score assigned is based on the size of the consensus when comparedto the total number of candidate locations for a POI.

FIG. 5A is a conceptual illustration of how the metric-based scoringsystem 220 would evaluate the XTR metric for an example of multiplepotential candidate locations in accordance with one embodiment. In FIG.5A, the candidate locations 100 a-100 i are used again as an example.First, the metric-based scoring system 220 identifies 408 each pair ofcandidate locations. Then, each identified pair of candidate locationsis evaluated against the map data to identify 409 whether there are anyroad segments between the two candidate locations.

To determine whether or not a road segment exists between twocandidates, metric-based scoring system 220 uses map data from map datastore 210. In one embodiment, map data is divided into multiple cells,and the cell(s) that include the coordinates of the candidates beingcompared are retrieved by system 220 from map data store 210. Forexample, GOOGLE's S2 library enables representation of latitude andlongitude within regions of defined areas (cells). A cell can be loadedinto memory and its contents (e.g., road segments and other features)easily identified. Other conventional technologies for representingcoordinates on the Earth also exist, as will be appreciated by those ofskill in the art, and can be sufficiently adapted for use as describedhere.

The metric-based scoring system 220 then determines 410 which candidatelocations are a members of the most candidate pairs with no intermittentroad segments. For example in FIG. 5A, to evaluate candidate location100 a, the metric-based scoring system 220 identifies that it is amember of eight pairs (indicated by the lines between 100 a and each ofthe other candidate locations). It then identifies that, of the eightpairs of which 100 a is a member, there are no intermittent roadsegments between the candidate locations in the pair for three of thoseeight pairs (indicated by solid 500 as opposed to dotted 502 lines).Therefore, in the example of FIG. 5A, candidate location 100 a would bea member of a group of four candidate locations on the same side of theroad. Because there are a total of nine candidate locations fourcandidate locations on the same side of the road would not constitute amajority consensus. However, the situation shown in FIG. 5A wouldconstitute a plurality consensus. If candidate locations 100 h and 100 iwere not included and instead candidate location 100 a was on the sameside of the road as four of the seven total candidate, then candidatelocation 100 a would be a member of a majority consensus of candidatelocations and would receive a corresponding score. The process ofidentifying pairs would be completed for each candidate location for aPOI. One of skill in the art will recognize that other methods may beused to determine XTR consensus (e.g. by identifying city blocks andcounting the number of candidate locations in each city block), and anyof these methods may be used depending on the efficiency requirementsand available map data for a particular implementation.

Once each candidate position has been evaluated, an XTR score is appliedto each candidate location. The score may be binary by applying onescore to points outside of the consensus and another to those that are amember of the consensus group. Alternatively, three different scores maybe applied based on whether the candidate location is a member of amajority consensus, plurality consensus, or no consensus at all.Finally, an XTR score may be applied based on the number of candidatelocations on a particular side of the road, the score may be directlyproportional to this number or otherwise related.

Building Footprint (BF) Consensus: The BF consensus metric is similar tothe XTR consensus metric, but instead of determining consensus for aparticular side of a street, a consensus is determined within particularbuildings. This metric may only be calculated for embodiments that haveaccess to more detailed map data that includes building footprints. TheBF consensus metric may also apply a score to candidate locations thatare a member of a local consensus of candidate positions on a particularside of a road or plurality consensus as discussed in relation to theXTR consensus metric.

FIG. 5B is a conceptual illustration of determining the BF metric for anexample of multiple potential candidate locations in accordance with oneembodiment. In FIG. 5B the same example candidate positions 100 a-100 iare used. In this example, there are three groups of candidate locationsforming a local BF consensus 504, 506, and 508. Each of these groups ofcandidate locations are located on a separate side of the road from theother candidate locations and, of the candidate locations on that sideof the road, they are a member of a majority BF consensus. Meanwhile,candidate locations 100 e-100 g are members of a plurality BF consensussince three of the nine candidate locations are located in building 104c and a maximum of only two candidate locations are located in buildings104 a, 104 b, and 104 d.

To evaluate the BF consensus metric the metric-based scoring system 220identifies 414 building footprints containing at least one candidatelocation and determines 416 the number of candidate locations withineach identified footprint. The metric-based scoring system 220 thenidentifies 418 the candidate locations contained within the buildingfootprint having the most candidate locations and applies 420 a BF scoreto the identified candidate locations.

However, depending on the embodiment, different scoring schemes can beused. For example, as described above the metric-based scoring system220 may identify local consensuses and apply a separate score formembers of those local consensuses, while applying a greater score to aplurality or majority consensus. Additionally, another score could beapplied to any candidate locations that are inside of a buildingfootprint but not part of any consensus, depending on the embodiment.

Distance-from-road (DFR): The DFR metric applies a score to a candidatelocation based on the distance between the candidate location and thenearest road segment 422. To calculate the DFR metric for each candidatelocation, the metric-based scoring system 220 calculates 424 thehaversine distance between a candidate location and the nearest point ofthe closest road segment. The metric-based scoring system 220 thenapplies 426 a score for each candidate location based on the calculateddistance. The score may be proportional to the distance or otherwiserelated. FIG. 5C is a conceptual illustration of determining the nearestroad segment to calculate the DFR, NSS, and NSP metrics for an exampleof multiple potential candidate locations in accordance with oneembodiment. The distance between each candidate location 100 a-100 i androad segments 102 a-102 d are represented by the dotted lines in FIG.5C.

Nearest-same-segment (NSS) Consensus: NSS consensus is a metric thatapplies a score to a candidate location based on whether the candidatelocation is a member of consensus group of candidate locations thatshare the same nearest segment 428. The NSS consensus metric may applyscores in the same ways as the previously described consensus metricsregarding majority and plurality consensuses 430. In the example of FIG.5C, two candidate locations, 100 e and 100 g, are nearest to roadsegment 102 a, four candidate locations, 100 b-100 d and 100 f, arenearest to road segment 102 b, one candidate location 100 h is nearestto road segment 102 c, and two candidate locations, 100 a and 100 i, arenearest to road segment 102 d. Thus, for the example in FIG. 5Ccandidate locations 100 b-100 d and 100 f are members of a pluralityconsensus of candidate locations with the same nearest road segment. Themetric-based scoring system 220 applies 432 a NSS score in either abinary, categorical, or proportional manner as previously described withreference to the other consensus metrics.

Nearest Segment Popularity (NSP): The NSP metric assigns a score basedon the popularity of a nearest segment to each candidate location. ThePOI location determination engine 216 may determine 434 road segmentpopularity based on previously selected authoritative POI locations.Referring again to FIG. 5C, road segment 102 a may, for example, be thenearest road segment to seven other previously located POI locations,segment 102 b may be the nearest segment to fifteen other POI locations,segment 102 c may be the nearest segment to nine other POI locations,and segment 102 d may be the nearest segment to three POI locations. Inthis example, candidate locations 100 e and 100 g would be scored basedon the popularity of segment 102 a, candidate locations 100 b-100 d and100 f would be scored based on the popularity of segment 102 b and soon. Alternatively, segment popularity might be obtained from the mapdata store 210 (i.e. from the map data provider).

The metric-based scoring system 220 would then apply 436 an NSP score toeach of the candidate locations based on the determined nearest segmentpopularity. In some embodiments, the NSP score is applied based on therelative ranking of the segment popularity when compared to the otheridentified segments. Alternatively, the score is applied based on theabsolute segment popularity when compared to all segments in map datastore 210. The exact score applied depends on the implementation.

In addition the metrics discussed above, the metric-based scoring system220 may apply 438 other metrics not specifically described. One of skillin the art can imagine metrics similar to the consensus metricsdescribed above but instead based around other map features such asparking lots or smaller entities.

Once the candidate locations have been evaluated against all of themetrics, the scores applied for each metric are aggregated 440 by themetric-based scoring system 220. The aggregation of the scores may besimple sum of the individual scores for each candidate location.

Additionally, the score applied based on each metric may vary dependingon the type, general location, or any other property of the POI. Forexample, BF consensus candidates may receive a greater score in suburbanarea than in an urban one, while XTR consensus candidates might receivea more highly weighted score in urban areas.

Once the scores for each candidate location have been determined by themetric-based scoring system 220 the candidate with the highest score isselected 442 as the authoritative candidate. In some embodiments, themetric-based scoring system calculates a confidence estimate based onthe scores of the candidate locations and reports the calculatedconfidence value along with the selected candidate location to the POIlocation determination engine 216.

In some embodiments, the confidence score for an authoritative candidateselection is determined by applying the metric-based scoring system 220to a data set of candidate locations for POIs where the trueauthoritative candidate is known (discussed in more detail withreference to FIGS. 7A and 7B). An accuracy score is then determinedbased on the comparison and used as an estimate for the confidenceassociated with the selections of the metric-based scoring system 220.

FIG. 6 illustrates a method for training a machine learning model fordetermining an authoritative location in accordance with one embodiment.Before the machine learning model 218 can be applied 308 the POIlocation determination engine 216 must train the machine learning model218 using training data from training data store 214 and the methoddescribed in FIG. 6. Initially, the engine 216 obtains 602 candidatelocations and associated curated location data from training data store214 and obtains 604 map data from map data store 210. Thus, for each POIin the training data set, the engine 216 obtains a set of candidatelocations from the data providers and a curated location. The curatedlocation may be curated manually (e.g. by geotagging each POI) by theentity implementing system 200 and may be the exact location of theentrance of the POI or any other standardized location for the POI. Theposition of the curated location relative the whole area occupied by thePOI may vary depending on the type of the POI. For example, for a parkthe curated location might be located in the center of the park whilefor a café the implementer of system 200 may choose the curated locationto be the front entrance of the café. The curated location serves as thereference for determining the correct authoritative candidate locationin the training data, such that a loss function operating on the datacan evaluate the selection of the machine learning model 218.

Once engine 216 has accessed the appropriate data, engine 216 identifies606 all of the providers and provider-pair combinations in the trainingdata set. The engine 216 accomplishes this by determining the number andidentity for each of the data providers providing candidate locations tosystem 200. The engine 216 then determines the number of two membercombinations that are possible from the identified providers. This stepmust be completed in order to generate a feature vector for eachcandidate location received from a provider.

Engine 216 then applies 608 authority criteria for each POI in thetraining data set to determine the target candidate selection for themachine learning model. The authority criteria are a set of rules thatallow engine 216 to determine which of the candidate locations for a POIis the authoritative location while having access to the actual curatedlocation of the POI. FIG. 7A-7B are conceptual illustrations of theapplication of authority criteria for an example of multiple potentialcandidate locations and differing example curated locations inaccordance with one embodiment.

In FIGS. 7A and 7B candidate locations 100 a-100 i are used as anexample yet again. Additionally, the curated location 700 is alsoillustrated. In some embodiments, the authority criteria specify anauthority radius 702. Depending on the embodiment the authority radiusmay or may not extend across road segments. The authority criteria mayspecify that a target authoritative location must be within theauthority radius 702 around the curated location 700. In someembodiments, the closest candidate location within the authority radius702 is selected as the target location, which in this example would becandidate location 100 c. Alternatively, or additionally, an authoritycriteria may be that the target criteria is required to be within thesame building footprint 704 as the curated location 700. If theauthority radius 702 and the building footprint criteria 704 are ineffect then candidate location 100 b would be selected as the targetlocation because it lies within the authority radius 702 and within thebuilding footprint 704. Other authority criteria may also be appliedbased on map features or the relative positions of the candidatelocations and the curated location.

FIG. 7B illustrates an example where none of authority criteria aresatisfied by the candidate locations 100 a-100 i. In this case, allcandidate locations 100 a-100 i lie outside the authority radius 708around the curated location 706. Because, according to the authoritycriteria, none of the candidate locations are a good approximation ofthe actual location of the POI, an appropriately trained machinelearning model 218 may correctly classify a set of candidate locationsas being inconclusive regarding an authoritative location by indicatinga relatively high confidence in the “null provider.” The selection ofthe provider for a POI indicates that the search for an authoritativelocation was inconclusive and likely to lead to an incorrect location somore detailed location information should be obtained.

Returning to FIG. 6, once the authoritative criteria have been appliedto the training data set the target candidates will have been determinedfor each POI in the training data set. The subsequent step is then tocalculate 610 per-location features for each provided candidate locationfor each POI in the training data set. A feature vector for a candidatelocation contains two types of features: per-location features andper-pair features. In an embodiment that utilizes binary classificationon a per-provider basis, per-location features are included in featurevectors associated with the provider of the location to which theper-location feature applies. On the other hand, the same per-pairfeature may be included in two feature vectors: a feature vector foreach provider in the pair to which the per-pair feature applies. Thus,the same value of a per-pair feature would be present in two featurevectors, each vector associated with a different provider and used astraining data for a different decision tree.

In an alternative embodiment, where a multiclass classification GBDT isused, all per-location and per-pair features are used in a singlefeature vector for a POI. In this case, the value of each per-pairfeature is present only once per feature vector, where each vector isassociated with a single set of candidate locations for a POI. Thetraining data set is then used to calculate a multiclass decision treethat determines which provider provided the authoritative location for aPOI.

Per-location features are a category of features that are calculatedbased on the data for a candidate location alone, and typically not incomparison to other candidate locations, though there may be exceptions.For example, per-location features may include, but are not limited tothe following features:

On-the-road (OTR) Feature: The OTR features is a binary feature thatindicates whether a candidate location is located on a road segment oroff of a road segment.

Distance-from-road (DFR) Feature: The DFR feature is similar to the DFRmetric discussed above in that it indicates the distance from acandidate location to the nearest road segment.

POI Category Feature: The POI category feature is a categorical featureindicating what type of POI the candidate location is labeled as (e.g.café, restaurant, museum, park, etc.). The POI category feature may bedetermined on a per-provider basis as each provider may use differentPOI category labels. Alternatively, the POI category feature may beevaluated after normalization and therefore may be consistent acrosscandidate locations from different providers.

Inside-a-building (IB) Feature: The IB feature is a binary feature thatindicates whether or not the candidate location is located inside of abuilding footprint.

Per-pair features are features that are calculated 612 on eachcombination of candidate location pairs in a set of candidate locations.For example, if there are four POI data providers in a particular dataset, each provider providing a candidate location for each POI, thenthere would be six possible pairs of candidate locations for each POIand the feature vector would include three sets of per-pair features foreach candidate location. Thus, each per-pair feature characterizes aparticular aspect of a relationship between a pair of candidatelocations. Per-pair features may include, but are not limited to thefollowing:

Pair across-the-road (XTR) Feature: The pair XTR feature is a binaryfeature that indicates whether a pair of candidate locations are acrossa road segment from each other.

Pair Distance Feature: The pair distance features is a continuousfeature indicating how far apart a pair of candidate locations are fromeach other.

Same-building (SB) Feature: The SB feature is a binary feature thatindicates whether a pair of candidate locations are within the samebuilding footprint.

Pair Nearest-same-segment (NSS) Feature: The pair NSS feature is abinary feature that indicates whether a pair of candidate locations hasthe same nearest road segment.

In the binary classification embodiment, a feature vector for a singlecandidate location therefore includes all of the per-location featurevalues for that candidate location and all per-pair feature values forpairs of candidate locations that include the candidate location. One ofskill in the art will appreciate that additional or fewer features thanthose discussed above may be included in a feature vector depending onthe embodiment.

In embodiments utilizing multiclass classification, a single featurevector is used and therefore each per-pair feature is only included onceper-pair of data providers, as opposed to being included in the featurevector for each provider included in the pair.

Once feature vectors for each POI data set in the training data havebeen calculated 614, engine 216 may use a GBDT algorithm such as XGboostor another similar algorithm to calculate 616 a decision tree for eachprovider of POI data. The GBDT algorithm may include an objectivefunction for scoring model iterations that includes a loss term andregularization term. Other supervised learning algorithms typically usedfor classification may also be used, such as random-forest algorithms.In some embodiments, a GBDT is learned for each provider in the data setand each calculated GBDT would solve a binary classification problem forwhether each provider has provided an authoritative point. For example,if there are three POI data providers a GBDT will be trained for each ofthe three data providers to determine whether a candidate locationprovided by that provider is an authoritative point. In addition, a GBDTmay be trained for the case where no provider is an authoritative pointfor a total of four GBDTs, as explained in more detail below. Acandidate location would be incorrectly classified if it was classifiedas authoritative by a GBDT iteration when it was not the target locationfor a POI and would be classified correctly if it was the targetlocation for the POI. For continuous features such as the DFR feature,regression or another statistical technique may be used to determine avalue to for the GBDT to branch on.

In some binary classification embodiments, in addition to calculating aGBDT for each provider, a GBDT is also trained for the case that none ofthe providers have provided an authoritative candidate location. Thisprocess may be completed in the same way a GBDT is trained for eachprovider, by calculating feature vectors for the “null-provider” foreach POI in the training data set, and by calculating a GBDT for thenull provider. However, the feature vector for the null-provider optionmay differ from the provider feature vectors. Features for thenull-provider option may pertain to the general map environment aroundthe POI including but not limited to POI density in the general area,the POI type, and the type of the search area (e.g. urban, rural,suburban etc.). Additionally, summary statistics of the providerfeatures may be used as null-provider features, depending on theembodiment.

In some alternative embodiments, a multiclass decision tree iscalculated using a similar GBDT algorithm as discussed above, howeverinstead of solving a binary classification problem for each provider,the GBDT solves a multiclass classification problem for the set ofproviders. In this case, only one feature vector is needed per POI dataset. The resulting decision tree will directly indicate theauthoritative provider and an associated confidence value as discussedin more detail below.

After the above described training steps have been completed, engine 216outputs 618 the calculated GBDTs as the machine learning model 218. Ifsystem 200 receives 620 additional training data or map data, engine 216may restart the training process. In some embodiments, the trainingmethod is run periodically or after a threshold number of additionaltraining data points have been received.

FIG. 8 illustrates a method for determining an authoritative location ofa point of interest using a machine learning model in accordance withone embodiment. The machine learning model 218 begins by receiving 802candidate locations and associated data for a POI. The machine learningmodel may then obtain 804 map data from map data store 210. Once thedata has been obtained, machine learning model 218 identifies 806 theprovider for each received candidate position so that the appropriatefeature vector can be calculated and the associated GBDT can be used toclassify the candidate position as authoritative.

Once each candidate position has been matched to a provider, the machinelearning model 218 may calculate 808 a single feature vector based onthe candidate positions and associated information of the POI forembodiments that utilize a multiclass classification tree.Alternatively, in embodiments utilizing multiple binary classificationtrees, the machine learning model 218 may calculate 814 a feature vectorfor each identified provider or a candidate location.

In a multiclass classification embodiment, after calculating the singlefeature vector for the set of candidate locations for the POI, themachine learning model 218 applies 810 the multiclass decision tree tothe calculated feature vector. The multiclass decision tree outputs aclassification of the set of candidate locations thereby determining 812an authoritative candidate location from the set.

In binary classification embodiments, the GBDT associated with theidentified provider for the candidate position is applied 816 to thecalculated feature vector associated with the same provider. As aresult, the machine learning model 218 determines 818 the classificationfor whether each candidate location is authoritative ornon-authoritative with an associated confidence value. In someembodiments, the confidence value may be calculated based on prevalenceof the leaf of the GBDT corresponding to the feature vector of thecandidate location.

In some embodiments that include a null-provider, the same process ofcalculating 814 a feature vector for each provider is used for thenull-provider. In these embodiments, a binary decision tree is alsoapplied to the feature vector associated with the null provider. Themachine learning model 218 would then determine 818 a classification forwhether or not any candidate locations are authoritative, therebyevaluating the null-provider hypothesis.

In a binary classification embodiments, once all classification andassociated confidence values have been determined, the machine learningmodel 218 selects 820 an authoritative candidate location based onconfidence criteria. The confidence criteria may specify a confidencevalue threshold for the candidate locations. If more than one candidatelocation satisfies the confidence threshold the candidate location withthe greatest associate confidence value will be selected. Alternatively,if no candidate locations are classified as authoritative withsufficient confidence the null-provider may be selected instead.Alternatively, if the null-provider is classified as being the correctselection with high confidence, the confidence criteria may dictate thatthe null provider be selected over other confidence authoritativecandidates.

FIG. 9 is a diagram illustrating a computer system upon whichembodiments described herein may be implemented. For example, in thecontext of FIG. 2, system 200 may be implemented using a computer systemsuch as described by FIG. 9. System 200 may also be implemented using acombination of multiple computer systems as described by FIG. 9, witheach computer system implementing one or more of the components ofsystem 200. Multiple-computer-systems implementations include networkedsystems, such as a networked client-server system.

In one implementation, system 200 includes processing resources such asone or more processors 902, as well as main memory 904, read only memory(ROM) 906, a storage device 908, and a communication interface 910.System 200 includes the processor(s) 902 for processing information andmain memory 904, such as a random access memory (RAM) or other dynamicstorage device, for storing information and instructions to be executedby the processor(s) 902. Main memory 904 also may be used for storingtemporary variables or other intermediate information during executionof instructions to be executed by processor(s) 902. System 200 may alsoinclude ROM 906 or other static storage device for storing staticinformation and instructions for processor(s) 902.

The storage device 908, such as a magnetic disk or optical disk, isprovided for storing information and instructions. The communicationinterface 910 can enable system 200 to communicate with one or morenetworks (e.g., cellular network) through use of the network link(wireless or wireline). Using the network link, system 200 cancommunicate with one or more computing devices, and one or more servers.In an example embodiment, the communication interface 910 is configuredto communicate with one or more of the data sources 202, 204, 206 ofFIG. 2.

In some variations, system 200 can be configured to receive sensor data(e.g., such as GPS data) from one or more location tracking devices viathe network link. The sensor data can be processed by the processor 902and can be stored in, for example, the storage device 908. The processor902 can process the sensor data of a location tracking device in orderto determine the path of travel of a transportation vehiclecorresponding to the location tracking device. Extrapolated positioninformation can be transmitted to one or more service requestor devicesover the network to enable the service applications running on theservice requestor devices to use the position information to present avisualization of the actual movement of the transportation vehicles.

System 200 can also include a display device 912, such as a cathode raytube (CRT), an LCD monitor, or a television set, for example, fordisplaying graphics and information to a user. An input mechanism 914,such as a keyboard that includes alphanumeric keys and other keys, canbe coupled to system 200 for communicating information and commandselections to processor(s) 902. Other non-limiting, illustrativeexamples of input mechanisms 914 include a mouse, a trackball,touch-sensitive screen, or cursor direction keys for communicatingdirection information and command selections to processor(s) 902 and forcontrolling cursor movement on display device 912.

In an example embodiment, storage device 908 stores data normalizer 208,map data store 210, navigation subsystem 212, and the POI locationdetermination engine 216 components of FIG. 2 as computer executableinstructions. During operation, the processor(s) 902 executes theinstructions and loads the components into main memory 904. Theinstructions cause the processor(s) 902 to perform the method of FIG. 3.In this way, the processor(s) 902 coupled to main memory 904, read onlymemory (ROM) 906, storage device 908, and communication interface 910(as described below in greater detail) is a special-purpose processor.

Examples described herein are related to the use of system 200 forimplementing the techniques described herein. According to oneembodiment, those techniques are performed by system 200 in response toprocessor(s) 902 executing one or more sequences of one or moreinstructions contained in main memory 904. Such instructions may be readinto main memory 904 from another machine-readable medium, such asstorage device 908. Execution of the sequences of instructions containedin main memory 904 causes processor(s) 902 to perform the process stepsdescribed herein. In alternative implementations, hard-wired circuitrymay be used in place of or in combination with software instructions toimplement examples described herein. Thus, the examples described arenot limited to any specific combination of hardware circuitry andsoftware.

FIG. 10 is a diagram illustrating a mobile computing device upon whichembodiments described herein may be implemented as described above, forexample with respect to a rider device or driver device. In oneembodiment, a computing device 1000 may correspond to a mobile computingdevice, such as a cellular device that is capable of telephony,messaging, and data services. Examples of such devices includesmartphones, handsets or tablet devices for cellular carriers. Computingdevice 1000 includes a processor 1006, memory resources 1010, a displaydevice 1002 (e.g., such as a touch-sensitive display device), one ormore communication sub-systems 1012 (including wireless communicationsub-systems), input mechanisms 1004 (e.g., an input mechanism caninclude or be part of the touch-sensitive display device), and one ormore location detection mechanisms (e.g., GPS module) 1008. In oneexample, at least one of the communication sub-systems 1012 sends andreceives cellular data over data channels and voice channels.

The processor 1006 is configured with software and/or other logic toperform one or more processes, steps and other functions described withimplementations, such as those described herein. Processor 1006 isconfigured, with instructions and data stored in the memory resources1010, to operate a transportation system 100 as described herein. Forexample, instructions for operating the transportation system 100 todynamically determine pick-up and drop-off locations can be stored inthe memory resources 1010 of the computing device 1000.

The processor 1006 can provide content to the display 1002 by executinginstructions and/or applications that are stored in the memory resources1010. In some examples, one or more user interfaces can be provided bythe processor 1006, such as a user interface for the serviceapplication, based at least in part on the received position informationof the one or more transportation vehicles. While FIG. 10 is illustratedfor a mobile computing device, one or more embodiments may beimplemented on other types of devices, including full-functionalcomputers, such as laptops and desktops (e.g., PC).

In addition to the embodiments specifically described above, those ofskill in the art will appreciate that the invention may additionally bepracticed in other embodiments.

Within this written description, the particular naming of thecomponents, capitalization of terms, the attributes, data structures, orany other programming or structural aspect is not mandatory orsignificant unless otherwise noted, and the mechanisms that implementthe described invention or its features may have different names,formats, or protocols. Further, the system may be implemented via acombination of hardware and software, as described, or entirely inhardware elements. Also, the particular division of functionalitybetween the various system components described here is not mandatory;functions performed by a single module or system component may insteadbe performed by multiple components, and functions performed by multiplecomponents may instead be performed by a single component. Likewise, theorder in which method steps are performed is not mandatory unlessotherwise noted or logically required. It should be noted that theprocess steps and instructions of the present invention could beembodied in software, firmware or hardware, and when embodied insoftware, could be downloaded to reside on and be operated fromdifferent platforms used by real time network operating systems.

Algorithmic descriptions and representations included in thisdescription are understood to be implemented by computer programs.Furthermore, it has also proven convenient at times, to refer to thesearrangements of operations as modules or code devices, without loss ofgenerality.

Unless otherwise indicated, discussions utilizing terms such as“selecting” or “computing” or “determining” or the like refer to theaction and processes of a computer system, or similar electroniccomputing device, that manipulates and transforms data represented asphysical (electronic) quantities within the computer system memories orregisters or other such information storage, transmission or displaydevices.

The present invention also relates to an apparatus for performing theoperations herein. This apparatus may be specially constructed for therequired purposes, or it may comprise a general-purpose computerselectively activated or reconfigured by a computer program stored inthe computer. Such a computer program may be stored in a non-transitorycomputer readable storage medium, such as, but is not limited to, anytype of disk including floppy disks, optical disks, DVDs, CD-ROMs,magnetic-optical disks, read-only memories (ROMs), random accessmemories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, applicationspecific integrated circuits (ASICs), or any type of media suitable forstoring electronic instructions, and each coupled to a computer systembus. Furthermore, the computers referred to in the specification mayinclude a single processor or may be architectures employing multipleprocessor designs for increased computing capability.

The algorithms and displays presented are not inherently related to anyparticular computer or other apparatus. Various general-purpose systemsmay also be used with programs in accordance with the teachings above,or it may prove convenient to construct more specialized apparatus toperform the required method steps. The required structure for a varietyof these systems will appear from the description above. In addition, avariety of programming languages may be used to implement the teachingsabove.

Finally, it should be noted that the language used in the specificationhas been Bean principally selected for readability and instructionalpurposes, and may not have been selected to delineate or circumscribethe inventive subject matter. Accordingly, the disclosure of the presentinvention is intended to be illustrative, but not limiting, of the scopeof the invention.

We claim:
 1. A computer-implemented method for providing vehicle routingguidance to a point of interest, comprising: receiving, by at least oneprocessor from a plurality of data sources, point of interest dataincluding at least one or more candidate locations for the point ofinterest and identifying information for the point of interest;obtaining map data including at least locations of road segments in anarea surrounding the one or more candidate locations for the point ofinterest; for each of the one or more candidate locations: evaluatingfor the candidate location, based on the map data and the candidatelocation, a plurality of metrics, wherein one of the plurality ofmetrics is an across-the-road consensus metric; determining a metricscore corresponding to each of the plurality of metrics; and aggregatingthe metric scores to calculate an aggregate score for the candidatelocation; selecting a first candidate location from the one or morecandidate locations, the first candidate location having the highestaggregate score of the one or more candidate locations; providingvehicle routing guidance to the first candidate location.
 2. Thecomputer-implemented method of claim 1, further comprising: for the oneor more candidate locations: calculating a feature vector for the one ormore candidate locations, the feature vector corresponding to the pointof interest; applying a multiclass classifier to the calculated featurevector; classifying one of the one or more candidate locations asauthoritative using the applied classifier; and calculating a confidencevalue associated with the classification of the candidate location;selecting the classified candidate location as a second candidatelocation from the one or more candidate locations; selecting anauthoritative candidate location from the first selected candidatelocation and the second selected candidate location, based on theaggregate score of the first candidate location and the associatedconfidence value of the second candidate location; providing vehiclerouting guidance to the selected authoritative candidate location. 3.The computer-implemented method of claim 2, wherein the multiclassclassifier is a gradient boosted decision tree.
 4. Thecomputer-implemented method of claim 3, wherein the gradient boosteddecision tree is trained by: retrieving training data containing a setof one or more candidate locations for each of a plurality of points ofinterest and a curated location for each of the plurality of points ofinterest, wherein each candidate location of each set of one or morecandidate locations is associated with a different provider; obtainingmap data including at least road segment data; for each of the pluralityof points of interest in the training data: applying authority criteriato the set of one or more candidate locations associated with the pointof interest; responsive to determining that a candidate location of theset of one or more candidate locations satisfies the authority criteria,based on the curated location, the set of one or more candidatelocations associated with the point of interest, and the map data:selecting the candidate location as a target location for the point ofinterest; and responsive to determining that none of the set of one ormore candidate locations satisfies the authority criteria, based on thecurated location, the set of one or more candidate locations associatedwith the point of interest, and the map data: indicating that there isno target location for the point of interest; and applying a gradientboosting algorithm to train the gradient boosted decision tree thatoptimally classifies candidate locations in a set of one or morecandidate locations as target locations or non-target locations for eachpoint of interest in the training data, according to a loss function anda regularization function.
 5. The computer-implemented method of claim2, wherein the feature vector comprises a plurality of featuresincluding per-location features and per-pair features.
 6. Thecomputer-implemented method of claim 2, wherein the multiclassclassifier includes multiple binary classifiers associated with eachprovider of candidate locations.
 7. The computer-implemented method ofclaim 1, wherein the plurality of metrics further includes at least oneof: a building footprint consensus metric, a distance from the nearestroad metric, a nearest-same-segment consensus metric, or a nearestsegment popularity metric.
 8. A computer program product for providingvehicle routing guidance to a point of interest, the computer programproduct stored on a non-transitory computer-readable medium andincluding instructions that when executed cause a processor to carry outsteps comprising: receiving, by at least one processor from a pluralityof data sources, point of interest data including at least one or morecandidate locations for the point of interest and identifyinginformation for the point of interest; obtaining map data including atleast locations of road segments in an area surrounding the one or morecandidate locations for the point of interest; for the one or morecandidate locations: calculating a feature vector for the one or morecandidate locations, the feature vector corresponding to the point ofinterest; applying a multiclass classifier to the calculated featurevector; classifying one of the one or more candidate locations asauthoritative using the applied classifier; and calculating a confidencevalue associated with the classification of the candidate location;selecting the classified candidate location as a first candidatelocation from the one or more candidate locations; providing vehiclerouting guidance to the first candidate location.
 9. The computerprogram product of claim 8, further comprising: for each of the one ormore candidate locations: evaluating for the candidate location, basedon the map data and the candidate location, a plurality of metrics;determining a metric score corresponding to each of the plurality ofmetrics; and aggregating the metric scores to calculate an aggregatescore for the candidate location; selecting a second candidate locationfrom the one or more candidate locations, the second candidate locationhaving the highest aggregate score of the one or more candidatelocations; selecting an authoritative candidate location from the firstselected candidate location and the second selected candidate location,based on the aggregate score of the second candidate location and theassociated confidence value of the first candidate location; providingvehicle routing guidance to the selected authoritative candidatelocation.
 10. The computer program product of claim 9, wherein theplurality of metrics includes at least one of: an across-the-roadconsensus metric, a building footprint consensus metric, a distance fromthe nearest road metric, a nearest-same-segment consensus metric, or anearest segment popularity metric.
 11. The computer program product ofclaim 8, wherein the multiclass classifier is a gradient boosteddecision tree.
 12. The computer program product of claim 11, wherein thegradient boosted decision tree is trained by: retrieving training datacontaining a set of one or more candidate locations for each of aplurality of points of interest and a curated location for each of theplurality of points of interest, wherein each candidate location of eachset of candidate locations is associated with a different provider;obtaining map data including at least road segment data; for each of theplurality of points of interest in the training data: applying authoritycriteria to the set of one or more candidate locations associated withthe point of interest; responsive to determining that a candidatelocation of the set of one or more candidate locations satisfies theauthority criteria, based on the curated location, the set of one ormore candidate locations associated with the point of interest, and themap data: selecting the candidate location as a target location for thepoint of interest; and responsive to determining that none of the set ofone or more candidate locations satisfies the authority criteria, basedon the curated location, the set of one or more candidate locationsassociated with the point of interest, and the map data: indicating thatthere is no target location for the point of interest; and applying agradient boosting algorithm to train the gradient boosted decision treethat optimally classifies candidate locations as target locations ornon-target locations for each point of interest in the training data,according to a loss function and a regularization function.
 13. Thecomputer program product of claim 8, wherein the feature vectorcomprises a plurality of features including per-location features andper-pair features.
 14. The computer program product of claim 8, whereinthe multiclass classifier includes multiple binary classifiersassociated with each provider of candidate locations.
 15. A system forproviding vehicle routing guidance to a point of interest, comprising:at least one processor; a data normalizer module, executed by theprocessor, configured to receive from a plurality of data sources atleast three candidate locations for the point of interest, at least oneof the received candidate locations located across a road segment fromanother candidate; a point-of-interest location determination engine,executed by the processor, configured to perform the steps of:receiving, by at least one processor from the plurality of data sources,point of interest data including at least one or more candidatelocations for the point of interest and identifying information for thepoint of interest; obtaining map data including at least locations ofroad segments in an area surrounding the one or more candidate locationsfor the point of interest; for the one or more candidate locations:calculating a feature vector for the one or more candidate locations,the feature vector corresponding to the point of interest; applying amulticlass classifier to the calculated feature vector; classifying oneof the one or more candidate locations as authoritative using theapplied classifier; and calculating a confidence value associated withthe classification of the candidate location; selecting the classifiedcandidate location as a first candidate location from the one or morecandidate locations; providing vehicle routing guidance to the firstcandidate location.
 16. The system of claim 15, further comprising: foreach of the one or more candidate locations: evaluating for thecandidate location, based on the map data and the candidate location, aplurality of metrics; determining a metric score corresponding to eachof the plurality of metrics; and aggregating the metric scores tocalculate an aggregate score for the candidate location; selecting asecond candidate location from the one or more candidate locations, thesecond candidate location having the highest aggregate score of the oneor more candidate locations; selecting an authoritative candidatelocation from the first selected candidate location and the secondselected candidate location, based on the aggregate score of the secondcandidate location and the associated confidence value of the firstcandidate location; providing vehicle routing guidance to the selectedauthoritative candidate location.
 17. The system of claim 16, whereinthe plurality of metrics includes at least one of: an across-the-roadconsensus metric, a building footprint consensus metric, a distance fromthe nearest road metric, a nearest-same-segment consensus metric, or anearest segment popularity metric.
 18. The system of claim 15, whereinthe multiclass classifier is a gradient boosted decision tree.
 19. Thesystem of claim 18, wherein the gradient boosted decision tree istrained by: retrieving training data containing a set of one or morecandidate locations for each of a plurality of points of interest and acurated location for each of the plurality of points of interest,wherein each candidate location of each set of one or more candidatelocations is associated with a different provider; obtaining map dataincluding at least road segment data; for each of the plurality ofpoints of interest in the training data: applying authority criteria tothe set of one or more candidate locations associated with the point ofinterest; responsive to determining that a candidate location of the setof one or more candidate locations satisfies the authority criteria,based on the curated location, the set of one or more candidatelocations associated with the point of interest, and the map data:selecting the candidate location as a target location for the point ofinterest; and responsive to determining that none of the set of one ormore candidate locations satisfies the authority criteria, based on thecurated location, the set of one or more candidate locations associatedwith the point of interest, and the map data: indicating that there isno target location for the point of interest; and applying a gradientboosting algorithm to train the gradient boosted decision tree thatoptimally classifies candidate locations as target locations ornon-target locations for each point of interest in the training data,according to a loss function and a regularization function.
 20. Thesystem of claim 15, wherein the feature vector comprises a plurality offeatures including per-location features and per-pair features.